Elbourhamy Doaa Mohamed
Educational Technology and Computer Department, Faculty of Specific Education, Kafrelshiekh University, Egypt.
PeerJ Comput Sci. 2024 Jun 24;10:e2143. doi: 10.7717/peerj-cs.2143. eCollection 2024.
This research introduces an innovative intelligent model developed for predicting and analyzing sentiment responses regarding audio feedback from students with visual impairments in a virtual learning environment. Sentiment is divided into five types: high positive, positive, neutral, negative, and high negative. The model sources data from post-COVID-19 outbreak educational platforms (Microsoft Teams) and offers automated evaluation and visualization of audio feedback, which enhances students' performances. It also offers better insight into the sentiment scenarios of e-learning visually impaired students to educators. The sentiment responses from the assessment to point out deficiencies in computer literacy and forecast performance were pretty successful with the support vector machine (SVM) and artificial neural network (ANN) algorithms. The model performed well in predicting student performance using ANN algorithms on structured and unstructured data, especially by the 9th week against unstructured data only. In general, the research findings provide an inclusive policy implication that ought to be followed to provide education to students with a visual impairment and the role of technology in enhancing the learning experience for these students.
本研究介绍了一种创新的智能模型，该模型用于预测和分析虚拟学习环境中视障学生对音频反馈的情绪反应。情绪分为五种类型：高度积极、积极、中性、消极和高度消极。该模型从新冠疫情爆发后的教育平台（Microsoft Teams）获取数据，并提供音频反馈的自动评估和可视化，这提高了学生的表现。它还能让教育工作者更好地了解视障学生的电子学习情绪状况。在支持向量机（SVM）和人工神经网络（ANN）算法的支持下，评估中的情绪反应在指出计算机素养方面的不足并预测表现方面相当成功。该模型在使用ANN算法对结构化和非结构化数据进行学生表现预测时表现良好，尤其是在仅针对非结构化数据的第9周。总体而言，研究结果提供了一项包容性的政策启示，即应遵循该启示为视障学生提供教育以及技术在提升这些学生学习体验方面的作用。
